Project Summary Executive function (EF) refers to the collection of processes that control and regulate cognitive functioning to achieve flexible or goal-directed behavior. EF influences diverse developmental outcomes, including quality of life measures and academic achievement. In this way, EF can serve as a leverage point for improving general aspects of cognitive functioning through interventions, ultimately enhancing physical and financial health and reducing crime rates. However, there is a lack of theoretical models that can be used to explain EF processes and develop well-grounded interventions. This proposal aims to test a learning mechanism that we have previously proposed to explain central aspects of EF development: the dimensional label learning hypothesis. According to this hypothesis, learning labels for visual features and dimensions (e.g, color or shape) structures frontal-posterior cortical connectivity. These connections can then be used to guide cognitive processing toward task-relevant features of the visual world through the activation of labels. To shed light on this process, we will follow children longitudinally from ages 2 to 5. A battery of tasks will be administered to assess dimensional label learning and dimensional attention. Neural activity will be measured from frontal, temporal, and parietal cortices using functional near-infrared spectroscopy. Finally, dynamic neural field (DNF) simulations will be used to interpret the neural and behavioral data. Such models can be used to implement specific hypotheses about neurocognitive functioning and learning to assess model fit in an iterative fashion. In this way, a model can be developed to explain behavioral and neural data observed as children learn dimensional labels and attentional skills. This model can then provide an arena to test different hypotheses about the learning processes that give rise to changes in EF. We will use these data to examine: (1) whether dimensional label learning predicts the development of dimensional attention, (2) the neural basis of dimensional label learning, (3) whether the neural dynamics during dimensional label comprehension and production predict neural activation in dimensional attention tasks, (4) whether the learning and neurocognitive processes implemented by the DNF model explain the association between behavioral and neural data. The DNF model can be used to make predictions about the role of dimensional label learning in aspects of EF development.